Every maneuver of a vehicle redistributes risks between road users. While human drivers do this intuitively, autonomous vehicles allow and require deliberative algorithmic risk management. But how should traffic risks be distributed among road users? In a global experimental study in eight countries with different cultural backgrounds and almost 11,000 participants, we compared risk distribution preferences. It turns out that risk preferences in road traffic are strikingly similar between the cultural zones. The vast majority of participants in all countries deviates from a guiding principle of minimizing accident probabilities in favor of weighing up the probability and severity of accidents. At the national level, the consideration of accident probability and severity hardly differs between countries. The social dilemma of autonomous vehicles detected in deterministic crash scenarios disappears in risk assessments of everyday traffic situations in all countries. In no country do cyclists receive a risk bonus that goes beyond their higher vulnerability. In sum, our results suggest that a global consensus on the risk ethics of autonomous driving is easier to establish than on the ethics of crashing.

Accurate predictions of base stations' traffic load are essential to mobile cellular operators and their users as they support the efficient use of network resources and sustain smart cities and roads. Traditionally, cellular network time-series have been considered for this prediction task. More recently, exogenous factors such as points of presence and other environmental knowledge have been introduced to facilitate cellular traffic forecasting. In this study, we focus on smart roads and explore road traffic measures to model the processes underlying cellular traffic generation with the goal to improve prediction performance. Comprehensive experiments demonstrate that by employing road flow and speed, in addition to cellular network metrics, cellular load prediction errors can be reduced by as much as 56.5 %. The code and more detailed results are available on https://github.com/nvassileva/DataMatters.

Connectivity is a main driver for the ongoing megatrend of automated mobility: future Cooperative Intelligent Transport Systems (C-ITS) will connect road vehicles, traffic signals, roadside infrastructure, and even vulnerable road users, sharing data and compute for safer, more efficient, and more comfortable mobility. In terms of communication technology for realizing such vehicle-to-everything (V2X) communication, the WLAN-based peer-to-peer approach (IEEE 802.11p, ITS-G5 in Europe) competes with C-V2X based on cellular technologies (4G and beyond). Irrespective of the underlying communication standard, common message interfaces are crucial for a common understanding between vehicles, especially from different manufacturers. Targeting this issue, the European Telecommunications Standards Institute (ETSI) has been standardizing V2X message formats such as the Cooperative Awareness Message (CAM). In this work, we present V2AIX, a multi-modal real-world dataset of ETSI ITS messages gathered in public road traffic, the first of its kind. Collected in measurement drives and with stationary infrastructure, we have recorded more than 230 000 V2X messages from more than 1800 vehicles and roadside units in public road traffic. Alongside a first analysis of the dataset, we present a way of integrating ETSI ITS V2X messages into the Robot Operating System (ROS). This enables researchers to not only thoroughly analyze real-world V2X data, but to also study and implement standardized V2X messages in ROS-based automated driving applications. The full dataset is publicly available for noncommercial use at v2aix.ika.rwth-aachen.de.

But in some cases, we cant get static frame because lighting can change, or some objects will be moved by someone, or always exist movement, etc. In such cases we are saving some number of frames and trying to figure out which of the pixels are the same for most of them, then this pixels becoming part of background_layer. Difference generally in how we get this background_layer and additional filtering that we use to make selection more accurate.

This paper investigates statistical models for road traffic modeling. The proposed methodology considers road traffic as a (i) highdimensional time-series for which (ii) regeneration occurs at the end of each day. Since (ii), prediction is based on a daily modeling of the road traffic using a vector autoregressive model that combines linearly the past observations of the day. Considering (i), the learning algorithm follows from an l1-penalization of the regression coefficients. Excess risk bounds are established under the high-dimensional framework in which the number of road sections goes to infinity with the number of observed days. Considering floating car data observed in an urban area, the approach is compared to state-of-the-art methods including neural networks. In addition of being very competitive in terms of prediction, it enables to identify the most determinant sections of the road network.

Technology company Continental has acquired a minority stake in Israeli start-up company Cartica AI. Cartica AI develops software in the field of artificial intelligence (AI). The solutions from Cartica AI are designed to accelerate machine learning in the field of object recognition. In the future, automotive systems for automated and autonomous driving will be able to adapt to and handle new traffic situations more quickly. The parties have agreed not to disclose the investment amount.

Scientists of South Ural State University have developed a unique intelligent system for monitoring traffic flow using artificial intelligence, which does not require specific recording equipment and can work on almost any type of camera. The system instantly processes data received in real time, unlike existing programs in which processing incurs a delay of up to 10 to 15 minutes. An article on the results of the study was published in the Journal of Big Data. "We have proposed and implemented a modernized system for assessing traffic flows, based on the most recent advances in the detection and tracking of vehicles. Unlike existing analogs, our system recognizes and analyzes in real-time the direction of movement of vehicles with a maximum relative error of less than 10 percent. The closest analogs are able to determine the speed and classify vehicles in only one direction and with the condition of placing the cameras above the traffic flow with an accuracy of 80-90 percent. Operating a neural network allows you to generate up to 400 traffic parameters in real-time at each intersection," says project manager Vladimir Shepelev, associate professor at the Automotive Transport Department of the Polytechnic Institute SUSU.

Colombo Big Data Meetup August 2nd 2018 Let's analyze the world's reaction to road traffic 2. In a nutshell Social Developer Skills & Interests Recognitions 7 years experience Full stack developer Angular, Big Data enthusiast Automation fanboy Microsoft MVP Developer Technologies Top contributor in the world on Stackoverflow for #Angular, #Cosmosdb Web application architecture Business intelligence Big Data Visualization Azure platform 120 repositories on Stackblitz 4800 answers on Stackoverflow Github contributions D3 directives and more Open-source contributions Sajeetharan Sinnathurai Senior Tech Lead at 99X Technology A few things about me! 3. What is sentiment analysis? "computationally identify and categorize the opinions expressed in a piece of text; determine whether positive/neutral/negative toward a topic/product…" [Oxford Dict.] 4. Why it is so important? What is Logic Apps? • Visual designer without writing single line of code • Dozens of pre-built templates to get started • Out of box support for popular SaaS and on-premises apps • Use with custom API apps of your own • Biztalk APIs for expert integration scenarios 9. Cognitive services Vision Speech Knowledge Language Search "Give your apps a human side" 10. •Sentiment analysis •Key phrase extraction •Topic detection •Language detection 13. Are we? Give away What were the two main Azure resources presented in this session? What is the name of the NOSQL database that could replace MSSQL in the proposed solution?

The future of transportation in waterway-rich cities such as Amsterdam, Bangkok, and Venice -- where canals run alongside and under bustling streets and bridges -- may include autonomous boats that ferry goods and people, helping clear up road congestion. Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Senseable City Lab in the Department of Urban Studies and Planning (DUSP), have taken a step toward that future by designing a fleet of autonomous boats that offer high maneuverability and precise control. The boats can also be rapidly 3-D printed using a low-cost printer, making mass manufacturing more feasible. The boats could be used to taxi people around and to deliver goods, easing street traffic. In the future, the researchers also envision the driverless boats being adapted to perform city services overnight, instead of during busy daylight hours, further reducing congestion on both roads and canals.

We consider the traffic data reconstruction problem. Suppose we have the traffic data of an entire city that are incomplete because some road data are unobserved. The problem is to reconstruct the unobserved parts of the data. In this paper, we propose a new method to reconstruct incomplete traffic data collected from various traffic sensors. Our approach is based on Markov random field modeling of road traffic. The reconstruction is achieved by using mean-field method and a machine learning method. We numerically verify the performance of our method using realistic simulated traffic data for the real road network of Sendai, Japan.